Mehtap Tunaci

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A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of(More)
OBJECTIVE The purpose of this study was to determine whether contrast enhancement features on 3D volumetric gradient-recalled echoMR images allow differentiation of benign from malignant solitary pulmonary nodules. MATERIALS AND METHODS Forty patients with solitary pulmonary nodules (range of greatest diameter, 7-40 mm) detected on CT underwent unenhanced(More)
A fully automated software is introduced to facilitate MR mammography (MRM) examinations and overcome subjectiveness in diagnosis using normalized maximum intensity-time ratio (nMITR) maps. These maps inherently suppress enhancements due to normal parenchyma and blood vessels that surround lesions and have natural tolerance to small field inhomogeneities(More)
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